ArticleJournal of medical systems2018
Classification of Alzheimer's Disease Based on Eight-Layer Convolutional Neural Network with Leaky Rectified Linear Unit and Max Pooling.
Article in Journal of medical systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 3 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
79 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Deep learning for MRI-based acute and subacute ischaemic stroke lesion segmentation-a systematic review, meta-analysis, and pilot evaluation of key results.Frontiers in medical technology · 2025Pooled it
- An Exploration: Alzheimer's Disease Classification Based on Convolutional Neural Network.BioMed research international · 2022Pooled it
- Prevalence and Diagnosis of Neurological Disorders Using Different Deep Learning Techniques: A Meta-Analysis.Journal of medical systems · 2020Pooled it
- MRI neuroimaging-based Alzheimer's disease stage classification using deep neural network with convolutional block attention module and GAN-style noise injection.Scientific reports · 2026Article
- MssNet: An Efficient Spatial Attention Model for Early Recognition of Alzheimer's Disease.IEEE transactions on emerging topics in computational intelligence · 2025Article
- A new method for Tomicus classification of forest pests based on improved ResNet50 algorithm.Scientific reports · 2025Article
- A pipelined, resource-efficient convolutional neural network architecture for detecting and diagnosing Alzheimer's disease using brain sMRI.Frontiers in neuroscience · 2025Article
- The top 100 cited articles on artificial intelligence in Alzheimer's disease and mild cognitive impairment: a bibliometric analysis.Frontiers in aging neuroscience · 2025Article
- Advancing Alzheimer's Diagnosis with AI-Enhanced MRI: A Review of Challenges and Implications.Current neuropharmacology · 2025Review
- Improving compound-protein interaction prediction by focusing on intra-modality and inter-modality dynamics with a multimodal tensor fusion strategy.Computational and structural biotechnology journal · 2024Article
- AI for the prediction of early stages of Alzheimer's disease from neuroimaging biomarkers - A narrative review of a growing field.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024Review
- [An ensemble model for assisting early Alzheimer's disease diagnosis based on structural magnetic resonance imaging with dual-time-point fusion].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Brain tissue segmentation in neurosurgery: a systematic analysis for quantitative tractography approaches.Acta neurologica Belgica · 2024Review
- Adaptive 3DCNN-based Interpretable Ensemble Model for Early Diagnosis of Alzheimer's Disease.IEEE transactions on computational social systems · 2024Article
- Convolutional neural network based data interpretable framework for Alzheimer's treatment planning.Visual computing for industry, biomedicine, and art · 2024Article
- Article
- A maize seed variety identification method based on improving deep residual convolutional network.Frontiers in plant science · 2024Article
- Artificial intelligence approaches for early detection of neurocognitive disorders among older adults.Frontiers in computational neuroscience · 2024Article
- Comparison of the diagnostic accuracy of resting-state fMRI driven machine learning algorithms in the detection of mild cognitive impairment.Scientific reports · 2023Article
- Deep Learning-Based Prediction of Alzheimer's Disease Using Microarray Gene Expression Data.Biomedicines · 2023Article
19 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Alzheimer's disease (AD) is a progressive brain disease. The goal of this study is to provide a new computer-vision based technique to detect it in an efficient way. The brain-imaging data of 98 AD patients and 98 healthy controls was collected using data augmentation method. Then, convolutional neural network (CNN) was used, CNN is the most successful tool in deep learning. An 8-layer CNN was created with optimal structure obtained by experiences. Three activation functions (AFs): sigmoid, rectified linear unit (ReLU), and leaky ReLU. The three pooling-functions were also tested: average pooling, max pooling, and stochastic pooling. The numerical experiments demonstrated that leaky ReLU and max pooling gave the greatest result in terms of performance. It achieved a sensitivity of 97.96%, a specificity of 97.35%, and an accuracy of 97.65%, respectively. In addition, the proposed approach was compared with eight state-of-the-art approaches. The method increased the classification accuracy by approximately 5% compared to state-of-the-art methods.
Indexed as
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.